Geometric cues are better than edges and pairwise affinities.
We further compare the geometric cues with two other mid-level representations: 2D edge and PA.
The 2D edge map is extracted using the Holistically-nested Edge Detection (HED) model [1]}, which shows more robust performance across the datasets than more recent methods. The HED model is trained on the Berkeley Segmentation Dataset and Benchmark (BSDS500) dataset [2]} which contains 500 images with segmentation annotations. PA can be thought of as a learned object boundary predictor. [3]} trained it directly on RGB images and then grouped the predictions into object masks using a combination of traditional grouping methods [4]}, [5]}, [6]}.
We compare these four data modalities as the source of pseudo labeling.
From Table REF , we can see that depth and normals outperform 2D edge and PA on detecting novel objects.
We speculate that this is because 2D edge and PA mainly capture object boundaries, whereas depth and normals have an extra spatial understanding of the scene and can thus better detect objects in complex scenes.
Owing to their better pseudo labels, GOOD-Depth/Normal outperform GOOD-Edge/PA on the final AR\(_N\) @100, i.e., 39.0%/38.9% vs. 38.1%/37.1%, see Appendix .
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